{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# high-dimensional reacher\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def smooth(y, radius=2, mode='two_sided'):\n",
    "    if len(y) < 2*radius+1:\n",
    "        return np.ones_like(y) * y.mean()\n",
    "    elif mode == 'two_sided':\n",
    "        convkernel = np.ones(2 * radius+1)\n",
    "        return np.convolve(y, convkernel, mode='same') / \\\n",
    "               np.convolve(np.ones_like(y), convkernel, mode='same')\n",
    "    elif mode == 'causal':\n",
    "        convkernel = np.ones(radius)\n",
    "        out = np.convolve(y, convkernel,mode='full') / \\\n",
    "              np.convolve(np.ones_like(y), convkernel, mode='full')\n",
    "        return out[:-radius+1]\n",
    "\n",
    "\n",
    "def plot(x, data, color, label):\n",
    "    y_m=np.mean(data, axis=0)\n",
    "    y_std=np.std(data, axis=0)\n",
    "    y_upper=y_m+y_std\n",
    "    y_lower=y_m-y_std\n",
    "    plt.fill_between(\n",
    "    x, list(y_lower), list(y_upper), interpolate=True, facecolor=color, linewidth=0.0, alpha=0.3\n",
    ")\n",
    "    plt.plot(x, list(y_m), color=color, label=label)\n",
    "    \n",
    "plt.figure(figsize=(8,6))\n",
    "file_pre = './'\n",
    "y=np.load(file_pre+'rewards_lstm.npy')\n",
    "y_=np.load(file_pre+'rewards.npy')\n",
    "\n",
    "x=np.arange(len(y))\n",
    "episode_length = 150\n",
    "x = episode_length*x\n",
    "plt.plot(x, smooth(y), label = 'SAC_LSTM', color='b')\n",
    "plt.plot(x, smooth(y_), label = 'SAC', color='r')\n",
    "\n",
    "plt.xlabel('Samples')\n",
    "plt.ylabel('Reward')\n",
    "# plt.ylim(0)\n",
    "leg= plt.legend( loc=2)\n",
    "plt.grid()\n",
    "plt.savefig('reward_compare.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
